1. Field of the Invention
The present invention relates to the field of well logging. More specifically, the present invention relates to methods for detecting hydrocarbons in reservoirs using nuclear magnetic resonance data.
2. Background Art
Oil and gas exploration and production are very expensive operations. Any knowledge about the formations that can help reduce the unnecessary waste of resources in well drilling is invaluable. Because of this, the oil and gas industry has developed various tools capable of determining and predicting earth formation properties. Among different types of tools, nuclear magnetic resonance (NMR) instruments have been successfully used in a wide variety of applications. NMR instruments can be used to determine formation properties, such as the fractional volume of pore space and the fractional volume of mobile fluid filling the pore space. A general background of NMR well logging is described in U.S. Pat. No. 6,140,817.
Nuclear magnetic resonance is a phenomenon occurring in a selected group of nuclei having magnetic nuclear moments, i.e., non-zero spin quantum numbers. 1H (proton) is the species commonly detected in NMR well logging because of its natural abundance and sensitivity to NMR measurements. When these nuclei are placed in a magnetic field (Bo, “Zeeman field”), they each precess around the axis of the Bo field with a specific frequency, the Larmor frequency (ωo), which is a characteristic property of each nuclear species (gyromagnetic ratio, γ) and depends on the magnetic field strength (Bo) effective at the location of the nucleus, i.e., ωo=γBo.
Both water and hydrocarbons in earth formations produce detectable NMR signals. It is desirable that the signals from water and hydrocarbons be separable so that hydrocarbon-bearing zones may be identified. However, it is not always easy to distinguish which signals are from water and which are from hydrocarbons. Various methods have been proposed to separately identify water and hydrocarbon signals.
The differential spectrum (DSM) and shifted spectrum (SSM) methods proposed by Akkurt et. al. in “NMR Logging of Natural Gas Reservoirs” Paper N. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1995, compare T2 distributions derived from two Carr-Purcell-Meiboom-Gill (CPMG) measurements performed with different polarization times (DSM) or echo-spacings (SSM). A modification to these methods, known as time domain analysis (TDA), was later introduced by Prammer et al. in “Lithology-Independent Gas Detection by Gradient-NMR Logging,” SPE paper 30562, 1995. In TDA, “difference” data are computed directly in the time domain by subtracting one set of the measured amplitudes from the other. The difference dataset is then assumed to contain only light oil and/or gas. In TDA, relative contributions from light oil or gas are derived by performing a linear least squares analysis of the difference data using assumed NMR responses for these fluids. Both DSM and TDA assume that the water signal has substantially shorter T1 relaxation times than those of the hydrocarbons. This assumption is not always valid, however. Most notably, this assumption fails in formations where there are large pores or where the hydrocarbon is of intermediate or high viscosity. The SSM method and its successor, the enhanced diffusion method (EDM) proposed by Akkurt et. al. in “Enhanced Diffusion: Expanding the Range of NMR Direct Hydrocarbon Typing Applications”, Paper GG. Transactions of the Society of Professional Well Log Analysts (SPWLA) Annual Logging Symposium, 1998, separate gas, oil and water contributions based on changes in the T2 distributions that result from changes in the echo spacing of CPMG measurements. The methods are applicable in a limited range of circumstances and the accuracy of the result is significantly compromised by incomplete separation of water and hydrocarbon signals in the T2 domain. Moreover, these methods are designed to function with CPMG sequences. However, with the diffusion-based methods, CPMG pulse sequences provide poor signal to noise ratios due to the reduced number of echoes that can be measured. A strategy for combining and selecting these different NMR methods has been described recently by Coates et al. in U.S. Pat. No. 6,366,087 B1.
The second approach to NMR hydrocarbon detection is more generally applicable. These methods typically apply forward modeling to suites of NMR data acquired with different parameters. The suite of NMR data are typically acquired with different echo spacings (TE) or polarization times (WT), and sometimes acquired with different magnetic field gradients (G). There are currently two methods in this approach: the MACNMR proposed by Slijkerman et al., SPE paper 56768, “Processing of Multi-Acquisition NMR Data”, 1999, and the Magnetic Resonance Fluid characterization (MRF) method disclosed in U.S. Pat. No. 6,229,308 B1 issued to Freedman and assigned to the assignee of the present invention (“the Freedman patent”). The Freedman patent is hereby incorporated by reference.
The MRF method is capable of obtaining separate oil and water T2 distributions. This method uses a Constituent Viscosity Model (CVM), which relates relaxation time and diffusion rates to constituent viscosities whose geometric mean is identical to the macroscopic fluid viscosity. With the MRF method, estimates for water and hydrocarbon volumes are obtained by applying a forward model to simulate the NMR responses to a suite of NMR measurements acquired with different parameters. In addition to fluid volumes, the MRF method also provides estimates of oil viscosity. The MRF method represents the most comprehensive and accurate method currently available for NMR fluid characterization in well-logging. Unlike the above-mentioned methods, the MRF method is applicable to any suite of NMR measurements and is not limited to the CPMG sequences. In fact, it has been successfully applied to NMR measurements acquired with diffusion-editing (DE) sequences.
While the prior art methods are useful in predicting the presence of hydrocarbons in the formations, it is desirable to have simpler methods that can predict the presence of hydrocarbons in the formations from NMR data and are generally applicable to NMR data acquired with different pulse sequences.